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From "Vladimir Rodionov (JIRA)" <j...@apache.org>
Subject [jira] [Assigned] (HBASE-18872) Backup scaling for multiple table and millions of row
Date Mon, 25 Sep 2017 22:49:00 GMT

     [ https://issues.apache.org/jira/browse/HBASE-18872?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel

Vladimir Rodionov reassigned HBASE-18872:

    Assignee: Vladimir Rodionov

> Backup scaling for multiple table and millions of row
> -----------------------------------------------------
>                 Key: HBASE-18872
>                 URL: https://issues.apache.org/jira/browse/HBASE-18872
>             Project: HBase
>          Issue Type: Improvement
>            Reporter: Vishal Khandelwal
>            Assignee: Vladimir Rodionov
> I did a simple experiment of loading ~200 million rows on a table 1 and nothing in a
table 2. This test was done on a local cluster ~ approx 3-4 containers were running in parallel.
The focus of the test was not on how much time backup takes but on time spent on the table
were no data has been changed.
> *Table without Data -->*
> Elapsed:	44mins, 52sec
> Average Map Time	3sec
> Average Shuffle Time	2mins, 35sec
> Average Merge Time	0sec
> Average Reduce Time	0sec
> Map : 2052
> Reduce : 1
> *Table with Data -->*
> Elapsed:	1hrs, 44mins, 10sec
> Average Map Time	4sec
> Average Shuffle Time	37sec
> Average Merge Time	3sec
> Average Reduce Time	47sec
> Map : 2052
> Reduce : 64
> All above numbers are a single node cluster so not many mappers run in parallel. but
let's extrapolate this to 20 node cluster, with ~100 tables and data size to be backed up
various for approx 2000 Wals, let us say each 20 node can process 3 containers i.e 60 wals
in parallel. assume 3 sec are spent in each WALs i.e. 6000\ 60 sec -->  100 per table -->
10000 sec for all tables. 
> ~166 mins -->  ~2.7 hrs only for filtering.  This does not seem to be scale. (These
are just rough numbers from a basic test). As all parsing is O (m (WALS) * n (Tables))
> Main intend of this test is to see even the backup of very less churning table might
take good amount for just filtering the data. As number of table or data increases, this does
not seem scalable 
> Even i can see from our current cluster numbers easily close to 100 table, 200 millions
rows,  200 -300 GB.
> I would suggest that we should have filtering to parse WALs once and to segregate in
multiple WALs per table --> hFiles from per table wals. ( just a rough idea).

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